In retail, SI earns its keep in conversion, basket size, and margin. We build personalisation, semantic search, and demand forecasting that lift revenue per visitor and take cost out of fulfilment and support, measured against your numbers, not a benchmark.

Get a production-readiness review What makes e-commerce SI hard

01 · Use cases

Six ways we deploy SI in e-commerce.

From semantic search to demand forecasting, every engagement is scoped around your catalogue, your data, and the revenue levers that matter most.

01 · Use case

Semantic Product Search

Vector search over your catalogue. Customers find what they mean, not just what they typed. Handles synonyms, misspellings, and intent.

02 · Use case

Personalisation Engine

Real-time product recommendations based on browsing, purchase history, and contextual signals. Not collaborative filtering from 2012.

03 · Use case

SI Customer Service

LLM-powered support agents trained on your FAQs, policies, and product data. Handle returns, tracking, and product questions without a human.

04 · Use case

Demand & Inventory Forecasting

ML models that predict sell-through rates, seasonal demand, and stockout risk. Reduce overstock and lost sales simultaneously.

05 · Use case

Dynamic Pricing

Pricing models that respond to competitor prices, demand signals, and inventory levels in real time. With guardrails to protect margin floors.

06 · Use case

Review & Sentiment Intelligence

NLP pipelines over product reviews and support tickets. Surface quality issues, competitive intelligence, and NPS drivers automatically.

Straight answers

The short version.

How does SI personalisation work without feeling invasive to shoppers?

SI personalisation stays welcome when it uses signals shoppers expect a store to use, such as browsing in the current session, past purchases and stated preferences, and avoids inferences they would find surprising. A sound design ranks products in real time from those signals, blends in category context, and handles new products through content-based embeddings so fresh items are not invisible while their history builds up. Data collection follows privacy-first practices: clear consent, minimal retention and no sensitive attributes in targeting. Merchandisers need to see why a product was recommended and be able to override it, because a black box erodes trust inside the team as well as with customers. Octalcode built this kind of system for Butcher Box AI, a personalised meal-plan recommender whose suggestions trace back to signals the merchandising team can inspect and override. Impact is measured against a holdout group, using the store's own conversion and basket metrics rather than an industry benchmark.

How do you build demand forecasting for an online store?

Demand forecasting for an online store predicts sell-through for each product and location, so purchasing and replenishment decisions rest on expected demand rather than last season's guess. The inputs are order history, stock levels, promotions, prices, seasonality and supplier lead times, cleaned so that stockout periods are not read as zero demand. Models forecast at the level where decisions are made, often by product and time period, or by category for newer lines, and they output a range rather than a single number so buyers can see the risk. New products borrow patterns from similar items until their own history builds up. Forecasts feed reorder points and stockout alerts, and planners can adjust them when they know something the data does not, such as an upcoming campaign. Accuracy is tracked on a rolling basis against actual sales, and the model is retrained when error rises or the catalogue changes significantly.

02 · What makes e-commerce SI hard

Four challenges we solve first.

Catalogue scale breaks rule-based systems
Thousands of SKUs, attributes, and categories require ML, not if-else logic. We build for catalogue scale from day one, not as a retrofit.
Relevance is not recall
Returning 1,000 results is not helpful. SI search needs to understand intent and rank by business value, not just keyword match.
Personalisation without creepiness
Users want relevant suggestions, not surveillance. We design personalisation with privacy-first data practices built into the architecture.
Cold-start for new products
New SKUs have no history. Content-based embeddings and category priors handle cold-start without waiting for data to accumulate.

Proof, not promises

Twenty-plus SI systems shipped to production. One playbook, six industries, and a team that stays past launch.

SI systems live in production: 20+
Senior engineers & researchers: 21+
Upwork rating, 3 verified reviews: 5.0
Founded in Lahore: 2022

Verified Upwork clientSI product engagement · 5-star review

03 · Common questions

Before you brief us.

How do you improve our search relevance?: A semantic search upgrade runs in four stages: data audit, embedding model selection, index build, and A/B test setup. The first test cycle shows whether relevance and conversion move before you commit to a full rollout.
Can you integrate with our existing e-commerce platform?: Yes. We've built SI layers on top of Shopify, Magento, WooCommerce, and custom platforms. We expose SI features via API so your storefront doesn't need a full rebuild.
How do you handle a catalogue of 100k+ SKUs?: At that scale we typically use a two-stage retrieval pipeline: fast approximate nearest-neighbour search to retrieve candidates, followed by a reranking model that applies business rules and personalisation signals.
What does an SI customer service agent actually handle?: Order status, returns and refunds, product questions, and policy lookups: the repetitive queries that fill most retail support queues. Escalations route to human agents with full context.

Available for new engagements

Book a free e-commerce SI scoping call.

30 minutes, one of our seniors, no slide deck. By the end of the call you will know whether we are the right team, and if not, who is.

Get a production-readiness review Email info@octalcode.com

Or call +1 (512) 710-5701

Let's scope it.Instant answers · free project scoping